Ecological Modeling in Risk Assessment - Chapter 10 docx

© 2002 by CRC Press LLC CHAPTER 10 Ecosystem Models — Terrestrial Christopher E. Mackay and Robert A. Pastorok Terrestrial ecosystem models are defined as mathematical constructs that represent biotic and abiotic components in deserts, forests, grasslands, or other terrestrial environments. Often they include physical, chemical, biological, and ecological processes. They are spatially aggregated such that input parameters and model functions are independent of distance and relative position. The primary endpoints for terrestrial ecosystem models include: • Abundance of individuals within species or trophic guilds •Biomass • Productivity • Food-web endpoints (e.g.,þspecies richness, trophic structure) We review the following terrestrial ecosystem models (Table 10.1): •Desert • Desert competition model, a hierarchical model that describes the dynamics of two interacting species of mice in the genus Dipodomys (Maurer 1990) •Forest • FVS (forest vegetation simulator), which, like the other forest models listed next, projects forest development through time (USDA 1999) • FORCLIM (forest climate model) (Bugmann 1997; Bugmann and Cramer 1998) • FORSKA (Lindner et al. 1997, 2000; Lindner 2000) • HYBRID (Friend et al. 1993; 1997) • ORGANON (Oregon growth analysis and projection) (OSU 1999) • SIMA (Kellomäki et al. 1992) • TEEM (terrestrial ecosystem energy model) (Shugart et al. 1974) •Grassland • Energy flow for short grass prairie model, an energy flow model for grasslands (Jeffries 1989) • SAGE (system analysis of grassland ecosystems), a model of the dynamics of primary producers and consumers (Heasley et al. 1981) • SWARD, an air pollution model that predicts the impact of sulfur dioxide on grass species and grazing ruminants (White 1984) 1574CH10.fm Page 129 Tuesday, November 26, 2002 6:06 PM © 2002 by CRC Press LLC Table 10.1 Internet Web Site Resources for Terrestrial Ecosystem Models Model Name Description Reference Internet Web Site Hierarchical model of Dipodomys A model of the dynamics of two interacting species of mice Maurer (1990) N/A FVS A U.S. Department of Agriculture model for projecting forest development through time USDA (1999) http://www.fs.fed.us/fmsc/fvs/index.php http://eco.wiz.uni-kassel.de/ model_db/mdb/fvs.html FORCLIM A model for projecting forest development through time with stress functions that could be easily modified for toxic chemical effects Bugmann (1997); Bugmann and Cramer (1998) http://www.pik-potsdam.de/cp/chief/forclim.htm FORSKA A model for projecting forest development through time without functions to account for harvesting Lindner et al. (1997, 2000); Lindner (2000) http://www.pik-potsdam.de/cp/chief/forska.htm HYBRID A model for projecting forest development through time which incorporates a spatially aggregated version of the forest landscape model ZELIG Friend et al. (1993; 1997) http://www.wiz.uni-kassel.de/ model_db/mdb/hybrid.html ORGANON A model for projecting forest development through time developed specifically for several habitats in Oregon OSU (1999b) www.cof.orst.edu/cof/fr/research/organon/ http://eco.wiz.uni-kassel.de/ model_db/mdb/organon.html SIMA A model for projecting forest development through time Kellomäki et al. (1992) http://gis.joensuu.fi/research/silmu/juttu4.html TEEM A forest ecosystem model used in assessing energy use impacts Shugart et al. (1974) http://www.esd.ornl.gov/people/o’neill_bob/ index.html Energy Flow for Short Grass Prairie Model An energy flow model specific to short grass prairies that describes interactions between primary producers and nesting sparrows Jeffries (1989) N/A SAGE An air pollution model for predicting the impact of sulfur dioxide on grass species and grazing ruminants Heasley et al. (1981) http://eco.wiz.uni-kassel.de/ model_db/mdb/sage.html SWARD A model of the dynamic equilibrium between primary producers and consumers within a grassland ecosystem White (1984) N/A Multi-timescale community dynamics A model of species turnover in bird communities Russell et al. (1995) N/A Nested species subset analysis A model for analyzing patterns of nestedness of species subsets in a biological community Cook and Quinn (1998) N/A SPUR A model for simulating interactions among soils, plants, and grazing ungulates on rangeland Hanson et al. (1988) http://vernon.tamu.edu/taes/rlem/brochure.htm http://eco.wiz.uni-kassel.de/ model_db/mdb/spur.html Note: N/A - not available 1574CH10.fm Page 130 Tuesday, November 26, 2002 6:06 PM © 2002 by CRC Press LLC • Rangeland • SPUR (simulating production and utilization of rangeland), a multispecies plant growth and grazer model (Hanson et al. 1988). •Island • Multi-timescale community dynamics models, models of species turnover in bird communities (Russell et al. 1995) • Nested species subset analysis, a model for analyzing patterns of nestedness for subsets of species (Cook and Quinn 1998) In addition to the models listed above, INTASS (Emlen et al. 1992) is a general ecosystem model that can be applied to terrestrial as well as aquatic systems (see review under Chapter 9, Aquatic Ecosystem Models). MUSE (multistrata spatially explicit ecosystem modeling shell) is a modeling system for Windows that can be used to implement and compare a wide variety of forest models (JABOWA, FORET, FORSKA, and others) (http://biology.anu.edu.au/research-groups/ ecosys/muse/MUSE.HTM). DESERT COMPETITION MODEL The model of Maurer (1990) describes the dynamics between two species of Dipodomys (mice) within a Chihuahua desert scrub ecosystem. It was specifically designed to evaluate the role of extrinsic vs. intrinsic factors in the regulation of population dynamics and community structure. The availability of food is the primary extrinsic factor. Intrinsic factors include species recruitment, foraging efficiency, and reproductive rates. It is a bioenergetic model in which the two species of Dipodomys feed on a single homogeneous seed source. The model is parameterized on the basis of observations derived from a 20-ha plot located on the Cave Creek Bajada, 2þkm north of Portal, Arizona. The model is structured as a multicompartment construct in which species-specific biomass values are the principle state variables. Differential equations describe competition between species on the basis of relative transfer of metabolic energy from the food source into the reproductive functions for each species. The foraging capacity of each population is based on relative recruitment, relative assimilation efficiency, and relative foraging efficiency of individuals. Realism — MEDIUM — The design of the desert competition model is principally a bioenergetics model, in which feeding and reproduction represent the main biological processes that lead to competition between the two species. The predominant driving variable is the availability of food. No consideration is given to health factors (such as response to toxic chemicals) independent of food availability. Relevance — MEDIUM — The model’s primary utility in ecological risk assessment is in quantifying the results of perturbations directly affecting interspecies competition for food. The availability of other ecosystem models that address more than two species limits the application of this model. No explicit consideration of toxicity is included in the model, but functions could be added relatively easily to account for toxic chemical effects. Flexibility — LOW — Although the model relies on standard quantification techniques common in many bioenergetic and population models, it is specific to seed-eating rodents. Treatment of Uncertainty — LOW — Examples provided in Maurer (1990) were parameterized on a deterministic basis with no consideration of uncertainty. Degree of Development and Consistency — MEDIUM — The model is apparently not available as software. However, details provided in Maurer (1990) are sufficient to reproduce the simulation. Ease of Estimating Parameters — MEDIUM — General parameter estimation for the model would be reasonably easy to do by using bioenergetic principles. However, achieving accuracy in applica - tions to new cases would require parameterization of the basal, active, and reproductive metabolic rates for the two species and of the availability and caloric content of the food source. Empirical 1574CH10.fm Page 131 Tuesday, November 26, 2002 6:06 PM © 2002 by CRC Press LLC data would also be required to define the relation between food availability and fecundity of individuals. Regulatory Acceptance — LOW — This model has no regulatory status and has not been applied in a regulatory context. Credibility — LOW — This model is a multicompartment bioenergetic model with governing differ- ential equations formulated on the basis of standard algorithms. Although this approach is very common in ecosystem simulations, no other reference was found for applications of this particular model. Resource Efficiency — MEDIUM — Parameterization of this model is reasonably simple because of its reliance on bioenergetic principles. However, application would require programming of the algorithms in software form. FVS FVS is a nationally supported framework for standardized projection of forest growth and yield (USDA 1999) originally based on the prognosis model (Stage 1973; Wykoff et al. 1982). Geographic variants have been developed covering the major forestlands in the U.S. The FVS modeling system has been used extensively for developing silvicultural prescriptions, evaluating management scenar - ios, updating inventory information, and providing input to forest planning models (USDA 1999 and references therein). Additional capabilities include forecasting vegetative structure, assessing wildlife habitat, analyzing fire hazard, determining forest health risk, and monitoring ecological processes. Using a parallel processing extension to FVS (Crookston and Stage 1991) and the SUPPOSE inter- face (Crookston 1997), FVS can be used to model the development of each stand with dependence on characteristics of the surrounding forest and to generate landscape-level statistics. The FVS model simulates a wide range of forest cover types, species, size classes, and stand densities. It predicts live tree stocking, growth, yield, and mortality, including stand structural stage and crown cover statistics. FVS simulates the establishment of seedlings and stump sprouts. FVS differs from other forest-gap models in that some variants include simulation of the understory component, including the height and cover of grasses, forbs, and shrubs. Two advanced features of FVS distinguish it from other forest projection systems. First, FVS uses growth increment data to adjust growth functions to match measured trends, thereby self- calibrating the equations to input data. Second, using the event monitor in FVS (Crookston 1990), modelers can define variables to influence simulation results and report additional output values. It allows management activities to be scheduled conditionally, on the basis of changing stand conditions. In addition, yield forecasts and information about the dominant vegetation can be evaluated relative to functions of towns and regions to predict the impact of human activity upon the ecosystem and vice versa. The event monitor adds a robustness not found in other projection models. A graphical user interface and tabular output have been developed to allow easy interaction with the FVS model. Linkage to the stand visualization system enables graphical display of stand conditions (e.g., trees, shrubs, down material, fire dynamics). Realism — HIGH — FVS is one of the most highly developed forest-gap models. Although the parameters are generalized for broad forest categories, the integration of comparative empirical data from the Forest Service allows a high degree of realism. The images rendered through the standard visualization system provide a realistic representation of silvicultural treatments and management options. Relevance — MEDIUM — FVS does not specifically include relationships for considering the impact of toxic chemicals. However, the ability of FVS to predict forest structure on the basis of competitive growth parameters makes it potentially useful in ecosystem characterization. Functions for toxic chemical effects could be added relatively easily. Specific modules within FVS account for the effects of insect pests and fire. 1574CH10.fm Page 132 Tuesday, November 26, 2002 6:06 PM © 2002 by CRC Press LLC Flexibility — HIGH — FVS was specifically developed to model all major forest types throughout the continental U.S. It can process a single stand, multiple stands, or an entire landscape in a single run. Treatment of Uncertainty — HIGH — FVS can be run in either deterministic or stochastic mode. Degree of Development and Consistency — HIGH — FVS has a history of more than 25þyears. It is the only nationally recognized and supported forest growth and yield model maintained by the U.S. Forest Service. FVS is available as a free software package from USDA. Ease of Estimating Parameters — HIGH — FVS is self-calibrating, given a tree input list that includes either radial-increment core data or diameter measurements at two points in time. Keyword modifiers give users the added ability to adjust model output to observed values. The U.S. Forest Service provides links to regional data sets that make simulation setup easy. Regulatory Acceptance — HIGH — FVS is a model accepted by USDA for estimating potential stand productivity within the continental U.S. Credibility — HIGH — FVS has a long history of use. Furthermore, because of its development and use by the U.S. Forest Service, it has become the de facto credibility standard for all other commercial forestry models. It has stood the test of time and continues to evolve as forest management issues evolve. Resource Efficiency — MEDIUM — All forest-gap models provide roughly equal types of outputs. FVS scored medium in resource efficiency because, although it is highly reliant on site specific parameterization (for ecosystem characterization), its availability as a user-friendly software package makes its application relatively easy. FORCLIM FORCLIM is a forest model developed for Central Europe but also successfully applied in eastern and northwestern North America (Bugmann 1997; Bugmann and Cramer 1998; http://www.pik- potsdam.de/cp/chief/forclim.htm). It is designed to incorporate simple yet reliable functions of climatic influence on ecological processes. FORCLIM consists of three modules, each of which can be executed independently or in combination with the other modules. The primary ecological module, FORCLIM-P, simulates the population dynamics of forest trees. Size cohorts are simulated as opposed to individual trees. Usually, functional plant groups are modeled rather than individual species. Maximum tree growth is determined from an exponential growth curve modified by nutrient and light availability, summer temperature, and water availability. Nitrogen is the limiting nutrient for growth. Light availability to the canopy is calculated based on the Beer–Lambert function. The effect of summer temperature on tree growth is calculated by using a parabolic relation between annual summer-degree days and the growth rate of the trees. Water availability is expressed as a function of annual evapotranspiration deficits. Rates of establishment of species during succession are a function of light availability at the forest floor, browsing intensity, and minimum temperature. Tree mortality is modeled empirically on the basis of age-related and stress-induced mortality rates. Changes in stand biomass over time are simulated as a function of environmental conditions and specific stresses (by indirectly applying climatic factors through the stress-induced mortality rate). The second module of FORCLIM, FORCLIM-E, simulates the soil–water balance within the forest. It is an empirical scheme (often referred to as a bucket model) requiring parameterization of only monthly mean temperatures and monthly precipitation sums. Outputs for this module are realized evapotranspiration rates relative to precipitation and capacity during each month of the iteration. These are then used by FORCLIM-P as inputs to the stress-growth and stress-mortality functions. The third module, FORCLIM-S, simulates soil nutrient cycling and availability to forest trees. State variables for this module include available and unavailable nitrogen and phosphorus pools. The state variables are moderated by concentration-dependent functions that reflect temperature, 1574CH10.fm Page 133 Tuesday, November 26, 2002 6:06 PM © 2002 by CRC Press LLC water, and soil physical–chemical conditions. FORCLIM also includes unique functions for carbon turnover as a factor modifying the rates of nitrogen and phosphorus turnover. Realism — HIGH — FORCLIM represents one of the later-generation forest models. Its major strengths in comparison with predecessors such as FORECE (Kienast 1987) include more detailed and realistic simulations of both water and nutrient behavior. Relevance — MEDIUM — FORCLIM might be useful in ecological risk assessment for the long-term characterization of forest habitats. Both the stress-mortality and stress-growth functions could be modified to account for effects of toxic chemicals. However, related models, such as JABOWA, which is intended for forest landscape simulation, are probably better suited for ecological risk assessment because of their widespread use and inclusion of some stochastic functions (see Chapter 11, Landscape Models — Aquatic and Terrestrial). Flexibility — MEDIUM — FORCLIM was originally designed to model continental European forests. However, it has been successfully applied to forests in the eastern U.S. Adaptation of the model to new cases requires site specific parameterization of functions for growth and mortality, and partic - ularly drought resistance. Treatment of Uncertainty — LOW — The model as presented does not track uncertainty or variability. Degree of Development and Consistency — HIGH — FORCLIM exists as a software package and may be available from the authors. Results of a validation indicate that the model is generally successful in projecting forest dynamics in eastern North America, except toward the dry timberline in the southeastern U.S., where it failed to simulate the dominance of drought-adapted species and reduced aboveground biomass. Ease of Estimating Parameters — MEDIUM — FORCLIM is a relatively complex model requiring moderate effort for parameterization. When applied to forest ecosystems for which the model is intended, parameterization would be extremely efficient because many of the parameter values used in previous applications could be retained. Regulatory Acceptance — LOW — FORCLIM has no regulatory status and to our knowledge has not been applied in a regulatory context. Credibility — HIGH — FORCLIM is the latest version of the JABOWA type of forest-gap model. It has been used by numerous researchers and is therefore considered credible. Resource Efficiency — HIGH — When applied to the forest ecosystems for which it was designed, the level of effort and cost for implementation of FORCLIM would be relatively low. Data require - ments could be fulfilled by readily available sources. FORSKA FORSKA was originally developed to model forest dynamics in Scandinavia (Lindner et al. 1997, 2000; Lasch et al. 1999; Lindner 2000). It simulates the growth, regeneration, and mortality of individual trees in small forest patches. It differs from the earlier forest-gap models because it includes a greater range of mechanistic functions to model tree growth. The tree-volume index, a measure of growth, is derived through the integration of the difference between net assimilation rates in the leaves of the crown layer and the cost in terms of production and maintenance of sapwood for the entire tree. Total tree mass was not parameterized but instead was mathematically inferred from the product of the diameter at breast height, the overall height, the bole length, and an empirical scaling factor. This integral function also includes a resource depletion coefficient that models the overall loss of rate-limiting nutrients as related to plot maturation. Outputs from this module are provided in terms of net tree-volume gains. Another unique aspect of FORSKA is the inclusion of a functional competition subcomponent in the overall growth module. Each individual tree is assigned a height-to-diameter ratio that depends on the net difference in solar radiation intensity between the tops and the bottoms of the crowns. Hence, if a tree is in danger of being overtopped, it will allocate resources to increasing vertical growth and thereby increase its height-to-diameter ratio. Effects of these changes on tree volume are determined by using a series of scaling relationships. 1574CH10.fm Page 134 Tuesday, November 26, 2002 6:06 PM © 2002 by CRC Press LLC In FORSKA, tree mortality depends on empirical functions specific to the species and age of the tree. No consideration was given to modeling any extraneous factors in the determination of mortality rates. Realism — HIGH — FORSKA provides a greater amount of detail in its functional relationships compared with other forest-gap models. The consideration of metabolic energy balance and changes in morphology due to competition enhance its realism. Relevance — MEDIUM — FORSKA can simulate a variety of ecologically relevant endpoints, such as tree biomass, stand biomass and age structure, and species richness. FORSKA contains no functions to account for effects of toxic chemicals. In its original form, FORSKA was intended to model a natural forest system, and no modules were included to account for plot management activities such as harvesting. A more recent version has initialization and management routines to enable the simulation of managed forests (Lindner 1998). Therefore, modeling toxic chemical effects would require modification of functions describing physical perturbations. Flexibility — MEDIUM — In comparison with other forest models, FORSKA can be more flexibly applied to diverse forest environments. However, it still relies on a considerable number of empirically derived functions that might require restructuring and data intensive reparam- eterization. Treatment of Uncertainty — LOW — FORSKA does not track uncertainty or variability within its model structure. Degree of Development and Consistency — MEDIUM — FORSKA has been validated. The model is available as a software package from the author. Ease of Estimating Parameters — HIGH — Parameter estimation for FORSKA is easier than for other forest models because the empirically derived growth functions have been replaced with functional relationships whose parameterization may be retained if applied in similar forest envi - ronments. However, parameterizing the mortality curves on the basis of site- and species-specific empirical observations is still necessary. Regulatory Acceptance — LOW — FORSKA appears to have no regulatory status and appears not to have been applied in a regulatory context. Credibility — HIGH — FORSKA uses standard modeling techniques developed over many years and has been cited and used in other independent forest research programs. Resource Efficiency — LOW — Application of FORSKA was considered to be less efficient than that of other forest-gap models because of the inclusion of functional growth relationships. This increases the requirement for site specific data. HYBRID HYBRID is a multilevel ecosystem model that synthesizes a forest-gap model, an ecosystem process model, and a biophysiological photosynthesis model (Friend et al. 1993; 1997). The model predicts tree growth and species succession, with carbon and water fluxes between the forest and the atmosphere. HYBRID originated from the merger of the forest-gap model ZELIG with the eco - system process model FOREST-BGC. By combining these models, predictions of responses to environmental change can be made for both the biochemical processes of individual trees and forest community structure. In this forest model, growth of individual trees is simulated as carbon fixation and partitioning. State variables in HYBRID include carbon dioxide (CO 2 ) partial pressure (both regional as well as across leaf cuticular boundaries), relative humidity, precipitation, air temperature, tree morphological metrics, evapotranspiration and respiration factors, soil–water capacity, and overall carbon storage capacity. HYBRID is structured as a nested compartment model. At the ecosystem level, it closely resembles a spatially aggregated version of the ZELIG model iterated on an annual time-step basis. However, rather than relying on empirically based growth curves, HYBRID substitutes the FOREST- BGC routines for carbon fixation, respiration, and carbon allocation, which are iterated on a daily time-step basis. Thus, each tree is separately modeled with respect to daily transpiration, 1574CH10.fm Page 135 Tuesday, November 26, 2002 6:06 PM © 2002 by CRC Press LLC photosynthesis/carbon fixation, and respiration. Each individual tree is assigned a set of funda- mental physiological parameters, depending on species and state of development. These are used to calculate carbon/water dynamics for each individual. However, state variables describing the light environment are treated at the plot level. The photosynthesis component of FOREST-BGC has been replaced in HYBRID with a detailed photosynthesis and stomatal conductance model. The fluxes of water are summed across individuals in each plot for each day and subtracted from the soil water to derive the dynamics in soil–water potential. The net CO 2 assimilation rates are summed across days to give annual forest productivity, which in turn provides the growth parameters for the forest-gap model. Realism — HIGH — HYBRID is a highly refined forest model that realistically accounts for interactions from the biochemical level to the ecosystem level. Relevance — HIGH — The model endpoints, including metrics for forest community structure, are relevant for chemical risk assessments. HYBRID does not include any relationships for the consid - eration of physical or toxicological impacts. However, of the forest models reviewed, HYBRID would be among those easiest to modify to include such effects, particularly for factors affecting stomatal conductance, water availability, or photosynthesis rates. Flexibility — HIGH — HYBRID can be applied to all major forest types in the continental U.S. This model calculates leaf-level photosynthesis and stomatal conductance for any C 3 plant species, with minimal species-specific parameterization. Treatment of Uncertainty — LOW — HYBRID is deterministic and thus does not track uncertainty. Some sensitivity analysis of HYBRID has been done. Degree of Development and Consistency — MEDIUM — Validation for a white oak forest (Knoxville, Tennessee) and a lodgepole pine forest (Missoula, Montana) indicates a high level of accuracy, particularly with regard to predictions of productivity. HYBRID is not available as a commercial software package, and Friend et al. (1993, 1997) do not provide sufficient detail to replicate the model structure. Ease of Estimating Parameters — HIGH — HYBRID includes a large database from which to select default values, particularly for the biochemical parameters. In many cases, additional data would not be required for species-specific growth curves. Regulatory Acceptance — LOW — HYBRID has no regulatory status and appears not to have been applied within a regulatory context. Credibility — HIGH — HYBRID has a reasonable history of use, having been developed as a synthesis of an accepted forest-gap model (ZELIG) and well-developed physiological models. Resource Efficiency — MEDIUM — All forest-gap models within this category provide roughly equal types of outputs. HYBRID is reasonably easy to parameterize, but it is not available as software and therefore would have to be converted into an executable format. ORGANON ORGANON is an individual-based forest model that uses a list of trees, each with exact measure- ments, as input data to predict forest plot productivity (OSU 1999). The user can specify periods of growth in 5-year increments and management activities such as thinning, fertilizing, and pruning. For each of the requested activities, the individual trees are modified to reflect the effects of the management actions. The program produces stand statistics at each step as well as yield information after the final harvest of the stand. Results include time course of tree diameter, height, and structure (branching and wood quality), as well as overall stand density and likely species composition. ORGANON has been developed to model three habitats in Oregon: (1)þthe mixed conifer young growth; (2)þthe Douglas fir (Pseudotsuga menziesii), grand fir (Abies grandis), white fir (Abies concolor), ponderosa pine (Pinus ponderosa), sugar pine (Pinus lambertiana), and incense cedar (Calocedrus decurrens) forest; and (3)þthe young growth Douglas fir forest. The model can project development in both even-aged and uneven-aged stands ranging from 20 to 120 years of 1574CH10.fm Page 136 Tuesday, November 26, 2002 6:06 PM © 2002 by CRC Press LLC development. Parameter inputs include coded tree species, trunk diameter, tree height, crown ratio, and radial growth. ORGANON relies on empirically derived, species-specific growth curves (mod - ified for tree density) to project potential tree growth and forest productivity on the basis of these input parameters. The model is iterative but uses only a 5-year time-step. Realism — HIGH — ORGANON’s predictions are based on empirical growth and competition func- tions. For the ecosystems for which it was developed, the model is highly realistic. Relevance — MEDIUM — ORGANON is adequate for simulating forest plot composition and pro- ductivity. It includes algorithms to account for various types of physical disturbance, but not toxicity. Presumably, the functions for physical disturbance could be modified or additional functions added to account for toxic chemical effects. Flexibility — LOW — ORGANON was developed specifically for, and is only applicable to, forest types found in the northwestern U.S. Treatment of Uncertainty — LOW — ORGANON is deterministic and thus does not track uncertainty. Degree of Development and Consistency — HIGH — ORGANON is available as a software package from the University of Oregon’s School of Forestry. ORGANON has been validated in the forest types for which it was designed. Ease of Estimating Parameters — HIGH — Because it is highly specialized, ORGANON already has the necessary growth functions included in the model structure. Therefore, parameterization is limited to considerations of site specific parameters such as the inclusive type of forest, distribution of current tree size and structure, and projected management practices. Regulatory Acceptance — LOW — ORGANON has no regulatory status and appears not to have been applied within a regulatory context. Credibility — HIGH — ORGANON appears to have a reasonable history of use, having been developed over a number of years. Numerous publications cover its development and use. Resource Efficiency — HIGH — All forest-gap models within this category provide roughly equal types of outputs. ORGANON scored high because of its low parameterization requirements and availability as a user-ready software package. SIMA The SIMA ecosystem model is a forest-gap model for depicting community and production pro- cesses dynamics in a boreal forest ecosystem between the latitudes 60° and 70°þN, and the longitudes 20° and 32° E (Kellomäki et al. 1992). In this model, forest structure and productivity are controlled by temperature, light conditions, and the availability of nitrogen and water. It was intended to model not only the short-term changes associated with the availability of water and nutrients but also long-term changes associated with changes in climate. The model is parameterized for Scotch pine (Pinus sylvestris), Norway spruce (Picea abies), pendula birch (Betula pendula), pubescent birch (B. pubescens), aspen (Populus tremula), and grey alder (Alnus incana) in Finland. Ground-cover vegetation is also considered in the model. The model is run in annual iterations for a forest plot of 100þm 2 . The model incorporates four environmental subroutines describing site conditions in terms of temperature, moisture, frost, and decomposition. These are generalized over the forest stand as daily temperature sum, total soil moisture, available soil nitrogen, and duration of subzero tem - peratures. The model’s state variables track reproduction, plant growth, and mortality (indirectly). Allowances are made for the inclusion of management activities such as thinning, clear-cutting, and fertilization. The environmental subroutines are linked to the demographic subroutines, which determine tree population dynamics (birth, growth, and death of trees). Using a bootstrap tech - nique, the user can simulate these processes and the subsequent succession that takes place in the forest ecosystem. The probability of an event is a function of the current forest structure and seasonality. 1574CH10.fm Page 137 Tuesday, November 26, 2002 6:06 PM © 2002 by CRC Press LLC Realism — MEDIUM — SIMA was judged adequate for simulating forest plot productivity. Both the starting point and model relationships depend on empirical observations. Therefore, when applied in a comparable situation, the model should be reasonably realistic. Relevance — MEDIUM — SIMA calculates forest productivity, species composition, biomass, and other endpoints that are very relevant for ecological risk assessment. However, the model contains no state variables to track effects of physical disturbances (other than management activities) or of toxic chemicals. Presumably, the functions for management actions could be modified or additional functions added to account for toxic chemical effects. Flexibility — MEDIUM — SIMA provides some flexibility in that it is specific to tree species as opposed to forest type. It relies heavily on empirical relationships. To date, it has been parameterized solely for Finnish boreal forests. Treatment of Uncertainty — LOW — Although SIMA is run in a probabilistic manner, the presentation in Kellomäki et al. (1992) does not track uncertainty or provide probabilistic density functions as output. The development of probability density functions would require adding a second-order Monte Carlo analysis. Degree of Development and Consistency — HIGH — SIMA is available as a software package. Ease of Estimating Parameters — MEDIUM — For applications of SIMA within the context for which it was designed (Finnish boreal forests), parameter estimation would be relatively easy because the model has been calibrated with default parameters and relationships. Application to other species would require a moderate effort to reparameterize the model. Regulatory Acceptance — LOW — SIMA has no regulatory status and appears not to have been used in a regulatory context. Credibility — LOW — Although SIMA depends on standard methods used in forest-gap models, no apparent history of use for this particular construct exists. Resource Efficiency — HIGH — All forest-gap models within this category provide roughly equal types of outputs. SIMA was rated high primarily on the basis of ease of use owing to a high degree of development and ease of parameterization when applied within the context for which it was intended. TEEM TEEM is an ecological model for stimulating energy transfers in forests (Shugart et al. 1974). TEEM is a high-resolution construct with a recommended maximum period of simulation equal to 3 years. Model outputs include annual growth of individual trees, overall forest productivity, and relative energy balance between the three identified forest components: primary producers, consumers, and decomposers. The spatial scale for TEEM is the forest stand, which is assumed to have minimal heterogeneity. Although TEEM may be parameterized for any forest type, it is designed specifically to model an eastern deciduous forest. TEEM consists of three modules that simulate primary producers, consumers, and decomposers. The primary producer module consists of time-dependent differential equations for predicting gross photosynthesis and respiration. Gross photosynthesis is defined as a function of water potential, temperature, and physiological time (duration of solar irradiance). Net photosynthesis (carbon assimilation) is defined as functional gross photosynthesis minus the sum of maintenance respiration and energy required to complete photosynthesis. Respiration is modeled as an exponential function and is inversely related to temperature. Net photosynthesis is proportional to temperature and solar irradiance and is modeled as an asymptotic function as maximum photosynthetic rates are approached. Growth and development within the primary producer module are modeled as the integration of the productivity algorithms for three classes of plant tissue (leaves, boles, and roots) and for storage (unincorporated carbohydrates). The consumer module is an energy-balance construct, in which net biomass is modeled as a function of food intake rates and losses resulting from predation, maintenance respiration, and nonpredatory mortality. 1574CH10.fm Page 138 Tuesday, November 26, 2002 6:06 PM [...]... and an integrated model combining all modules was not available for review The primary production module consists of differential sums equations, which are integrated over time and applied with daily time-steps Nine species can be modeled simultaneously, and both intra- and interspecific competition are considered in the model Abiotic variables used in the plant growth model include daily minimum and... uncertain predictions and are not cost-effective for use in ecological risk assessment of toxic chemicals in terrestrial systems Rather, it would be more efficient to use available population models or landscape models that permit spatially explicit parameterization Ecosystem models are best used as heuristic tools for understanding basic ecological processes and identifying sources of uncertainty in predictions... controlling energy flows between them Because predator density is assumed not to directly affect prey density, accumulation modeling (as opposed to collision modeling) is applied in this model The food web consists of a single primary producer compartment, three separate consumers (representing three species of grasshopper), and five separate life stages within the sparrow population (adults, pre-laying... SIMA Mixed conifers Forest (type user-defined) USDA (1999 and references therein)b Bugmann and Cramer (1998) Lindner et al (1997, 2000); Lasch et al (1999); Lindner (2000) Friend et al (1993, 1997) OSU (1999) Kellomäki et al (1992) TEEM Forest (type user-defined) Forest Bavaria, Scandinavia, and other European areas Tennessee Oregon Continental United States User-defined Shugart et al (1974) Grassland... for year-to-year turnovers that were in effect transient events not representative of actual extinction This error function is quantified by comparing cumulative variation in species number with observed 4-year variation rates Realism — LOW — Multi-timescale models consistently underpredicted observed species turnover rates Therefore, a substantial moderating factor is not accounted for within the structure... applied in any situation involving physical, spatial, or temporal barriers limiting interactions between two or more components of a wildlife population or community Treatment of Uncertainty — LOW — These models are deterministic; although they are parameterized on the basis of the probability of extinction, probability density functions associated with these estimates are not conserved in the final estimates... the sparrow population (adults, pre-laying embryos, post-laying embryos, © 2002 by CRC Press LLC 1574CH10.fm Page 140 Tuesday, November 26, 2002 6:06 PM nestlings, and fledglings) Each sparrow life stage is assigned a specific duration on the basis of growth and reproductive cycling within a single summer season At an appointed time, all energy within a stage is transferred to the next level of development... Bugmann and Cramer (1998) Lindner et al (1997, 2000); Lindner (2000) Friend et al (1993, 1997) OSU (1999a,b) Kellomäki et al (1992) Shugart et al (1974) Jeffries (1989) Cook and Quinn (1998) ◆◆◆ - high ◆◆ - medium ◆ - low 1574CH10.fm Page 146 Tuesday, November 26, 2002 6:06 PM © 2002 by CRC Press LLC Table 10. 2 1574CH10.fm Page 147 Tuesday, November 26, 2002 6:06 PM Table 10. 3 Applications of Terrestrial... coast of Great Britain and the Republic of Ireland The second model is a dynamic model of island biogeography Community expansion rates are determined by nonlinear regression of the natural logarithm of the number of species against time In the model, changes in species number are described relative to variations in the probability of extinction An error component is also included in the dynamic model... extensively to forests throughout the U.S., including assessments of disturbances by fire and insects (see USDA 1999 for examples) risk assessment of toxic chemicals in terrestrial systems, further development of landscape models is likely to prove more useful than the development of existing ecosystem models Hence, none of the terrestrial ecological models reviewed (Table 10. 2) was recommended for further evaluation . capabilities include forecasting vegetative structure, assessing wildlife habitat, analyzing fire hazard, determining forest health risk, and monitoring ecological processes. Using a parallel processing. developing silvicultural prescriptions, evaluating management scenar - ios, updating inventory information, and providing input to forest planning models (USDA 1999 and references therein). Additional. Crassulacean acid- dependent succulents) Semi-arid rangeland in Texas and Colorado; user-defined Hanson et al. (1988) Island Multi-timescale community dynamics User-defined avian species User-defined